IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v31y2019ics1544612318302289.html
   My bibliography  Save this article

A note of techniques that mitigate floating-point errors in PIN estimation

Author

Listed:
  • Ke, Wen-Chyan
  • Chen, Hueiling
  • Lin, Hsiou-Wei William

Abstract

This study aims at the estimation of the probability of informed trading (PIN), which may fail for stocks with high levels of trading activities due to a computer's floating-point exception (FPE). In this paper, we discuss two solutions of adopting scaled trade counts and reformulating the likelihood to estimate PIN for actively traded stocks. This study shows that, although scaled data mitigates the impact of the FPE, the effectiveness of scaled data, however, appears to underperform when users adopt the unsuitable expression of the likelihood function. In contrast, the remedy of reformulating the likelihood is more stable.

Suggested Citation

  • Ke, Wen-Chyan & Chen, Hueiling & Lin, Hsiou-Wei William, 2019. "A note of techniques that mitigate floating-point errors in PIN estimation," Finance Research Letters, Elsevier, vol. 31(C).
    • Handle: RePEc:eee:finlet:v:31:y:2019:i:c:s1544612318302289
    DOI: 10.1016/j.frl.2018.12.017
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612318302289
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2018.12.017?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Duarte, Jefferson & Young, Lance, 2009. "Why is PIN priced?," Journal of Financial Economics, Elsevier, vol. 91(2), pages 119-138, February.
    2. David Easley & Robert F. Engle & Maureen O'Hara & Liuren Wu, 2008. "Time-Varying Arrival Rates of Informed and Uninformed Trades," Journal of Financial Econometrics, Oxford University Press, vol. 6(2), pages 171-207, Spring.
    3. Akay, Ozgur (Ozzy) & Cyree, Ken B. & Griffiths, Mark D. & Winters, Drew B., 2012. "What does PIN identify? Evidence from the T-bill market," Journal of Financial Markets, Elsevier, vol. 15(1), pages 29-46.
    4. Lai, Sandy & Ng, Lilian & Zhang, Bohui, 2014. "Does PIN affect equity prices around the world?," Journal of Financial Economics, Elsevier, vol. 114(1), pages 178-195.
    5. Jackson, David, 2013. "Estimating PIN for firms with high levels of trading," Journal of Empirical Finance, Elsevier, vol. 24(C), pages 116-120.
    6. Easley, David & O'Hara, Maureen, 1987. "Price, trade size, and information in securities markets," Journal of Financial Economics, Elsevier, vol. 19(1), pages 69-90, September.
    7. William Lin, Hsiou-Wei & Ke, Wen-Chyan, 2011. "A computing bias in estimating the probability of informed trading," Journal of Financial Markets, Elsevier, vol. 14(4), pages 625-640, November.
    8. Ersan, Oguz & Alıcı, Aslı, 2016. "An unbiased computation methodology for estimating the probability of informed trading (PIN)," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 43(C), pages 74-94.
    9. Aslan, Hadiye & Easley, David & Hvidkjaer, Soeren & O'Hara, Maureen, 2011. "The characteristics of informed trading: Implications for asset pricing," Journal of Empirical Finance, Elsevier, vol. 18(5), pages 782-801.
    10. David Easley & Marcos M. López de Prado & Maureen O'Hara, 2012. "Flow Toxicity and Liquidity in a High-frequency World," The Review of Financial Studies, Society for Financial Studies, vol. 25(5), pages 1457-1493.
    11. Easley, David, et al, 1996. "Liquidity, Information, and Infrequently Traded Stocks," Journal of Finance, American Finance Association, vol. 51(4), pages 1405-1436, September.
    12. Ke, Wen-Chyan & Lin, Hsiou-Wei William, 2017. "An Improved Version of the Volume-Synchronized Probability of Informed Trading," Critical Finance Review, now publishers, vol. 6(2), pages 357-376, September.
    13. Quan Gan & Wang Chun Wei & David Johnstone, 2017. "Does the Probability of Informed Trading Model Fit Empirical Data?," The Financial Review, Eastern Finance Association, vol. 52(1), pages 5-35, February.
    14. Quan Gan & Wang Chun Wei & David Johnstone, 2015. "A faster estimation method for the probability of informed trading using hierarchical agglomerative clustering," Quantitative Finance, Taylor & Francis Journals, vol. 15(11), pages 1805-1821, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fabrizio Pappalardo & Alberto Moscatello & Gianmario Ledda & Anna Chiara Uggenti & Raffaella Gerboni & Andrea Carpignano & Francesco Di Maio & Riccardo Mereu & Enrico Zio, 2021. "Quantification of Uncertainty in CFD Simulation of Accidental Gas Release for O & G Quantitative Risk Assessment," Energies, MDPI, vol. 14(23), pages 1-16, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ersan, Oguz & Alıcı, Aslı, 2016. "An unbiased computation methodology for estimating the probability of informed trading (PIN)," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 43(C), pages 74-94.
    2. Griffin, Jim & Oberoi, Jaideep & Oduro, Samuel D., 2021. "Estimating the probability of informed trading: A Bayesian approach," Journal of Banking & Finance, Elsevier, vol. 125(C).
    3. Petchey, James & Wee, Marvin & Yang, Joey, 2016. "Pinning down an effective measure for probability of informed trading," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 456-475.
    4. Cosmin Octavian Cepoi & Victor Dragotă & Ruxandra Trifan & Andreea Iordache, 2023. "Probability of informed trading during the COVID-19 pandemic: the case of the Romanian stock market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-27, December.
    5. David Abad & M. Fuensanta Cutillas†Gomariz & Juan Pedro Sánchez†Ballesta & José Yagüe, 2018. "Does IFRS Mandatory Adoption Affect Information Asymmetry in the Stock Market?," Australian Accounting Review, CPA Australia, vol. 28(1), pages 61-78, March.
    6. Thomas Pöppe & Michael Aitken & Dirk Schiereck & Ingo Wiegand, 2016. "A PIN per day shows what news convey: the intraday probability of informed trading," Review of Quantitative Finance and Accounting, Springer, vol. 47(4), pages 1187-1220, November.
    7. Lof, Matthijs & Bommel, Jos van, 2018. "Asymmetric information and the distribution of trading volume," Research Discussion Papers 1, Bank of Finland.
    8. repec:zbw:bofrdp:001 is not listed on IDEAS
    9. Agudelo, Diego A. & Giraldo, Santiago & Villarraga, Edwin, 2015. "Does PIN measure information? Informed trading effects on returns and liquidity in six emerging markets," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 149-161.
    10. Kitamura, Yoshihiro, 2016. "The probability of informed trading measured with price impact, price reversal, and volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 42(C), pages 77-90.
    11. repec:zbw:bofrdp:2018_001 is not listed on IDEAS
    12. Lof, Matthijs & van Bommel, Jos, 2023. "Asymmetric information and the distribution of trading volume," Journal of Corporate Finance, Elsevier, vol. 82(C).
    13. Ping-Chen Tsai & Chi-Ming Tsai, 2021. "Estimating the proportion of informed and speculative traders in financial markets: evidence from exchange rate," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(3), pages 443-470, July.
    14. Chia, Yee-Ee & Lim, Kian-Ping & Goh, Kim-Leng, 2020. "More shareholders, higher liquidity? Evidence from an emerging stock market," Emerging Markets Review, Elsevier, vol. 44(C).
    15. Kim, Sangwan & Lim, Steve C., 2017. "Earnings comparability and informed trading," Finance Research Letters, Elsevier, vol. 20(C), pages 130-136.
    16. Schreder, Max, 2018. "Idiosyncratic information and the cost of equity capital: A meta-analytic review of the literature," Journal of Accounting Literature, Elsevier, vol. 41(C), pages 142-172.
    17. Duarte, Jefferson & Hu, Edwin & Young, Lance, 2020. "A comparison of some structural models of private information arrival," Journal of Financial Economics, Elsevier, vol. 135(3), pages 795-815.
    18. Emily Lin & Chu-Lan Michael Kao & Natasha Sonia Adityarini, 2021. "Data-driven tree structure for PIN models," Review of Quantitative Finance and Accounting, Springer, vol. 57(2), pages 411-427, August.
    19. Dean Katselas & Baljit K. Sidhu & Tom Smith & Chuan Yu, 2019. "Independently Certified Industry‐specific Disclosures to the Capital Market: The JORC Code in the Australian Mining Industry," Abacus, Accounting Foundation, University of Sydney, vol. 55(1), pages 128-179, March.
    20. Tiniç, Murat & Savaser, Tanseli, 2020. "Political turmoil and the impact of foreign orders on equity prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    21. Chang, Sanders S. & Wang, F. Albert, 2015. "Adverse selection and the presence of informed trading," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 19-33.
    22. Mazza, Paolo, 2015. "Price dynamics and market liquidity: An intraday event study on Euronext," The Quarterly Review of Economics and Finance, Elsevier, vol. 56(C), pages 139-153.

    More about this item

    Keywords

    PIN; Maximum likelihood; Scaled trade counts; Floating-point exception;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:31:y:2019:i:c:s1544612318302289. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.